A robust textual analysis of the dynamics of Hong Kong property market

Abstract

Market sentiments influence the dynamics of Hong Kong’s macro‐critical property market, but the unobservable nature of market sentiments makes it difficult to systemically assess this sentiment channel. Using text mining techniques, this paper sets up a news‐based property market sentiment index and a Google Trends‐based buyer incentive index for Hong Kong and studies the sentiment channel of transmission in the Hong Kong property market. The news‐based property market sentiment index can reflect the change in sentiments in past key events, with the sentiments in the primary market tending to lead that of the secondary market during the low housing supply period. For the Google Buyer Incentive Index, we find that it has value‐added in forecasting (or nowcasting) the official property price index. In mapping out the sentiment channel using a structural vector‐autoregressive model, we find that an improvement in market sentiments could stimulate buyers’ incentives, which then together would affect property prices and transaction volumes.

FinEntity: Entity-level Sentiment Classification for Financial Texts

“…We further demonstrate the practical utility of FinEntity in investment and regulatory applications, extending the work of Ashwin et al (2021); Wong et al (2022). Collaborating with a regulatory agency, we apply the fine-tuned PLMs to a unique cryptocurrency news dataset.…”